A substantial proportion of chronic disease patients do not respond to self-management interventions, which suggests that one size interventions do not fit all, demanding more tailored interventions. To compose more individualized strategies, we aim to increase our understanding of characteristics associated with patient activation for self-management and to evaluate whether these are disease-transcending. A cross-sectional survey study was conducted in primary and secondary care in patients with type-2 Diabetes Mellitus (DM-II), Chronic Obstructive Pulmonary Disease (COPD), Chronic Heart Failure (CHF) and Chronic Renal Disease (CRD). Using multiple linear regression analysis, we analyzed associations between self-management activation (13-item Patient Activation Measure; PAM-13) and a wide range of socio-demographic, clinical, and psychosocial determinants. Furthermore, we assessed whether the associations between the determinants and the PAM were disease-transcending by testing whether disease was an effect modifier. In addition, we identified determinants associated with low activation for self-management using logistic regression analysis. We included 1154 patients (53% response rate); 422 DM-II patients, 290 COPD patients, 223 HF patients and 219 CRD patients. Mean age was 69.6±10.9. Multiple linear regression analysis revealed 9 explanatory determinants of activation for self-management: age, BMI, educational level, financial distress, physical health status, depression, illness perception, social support and underlying disease, explaining a variance of 16.3%. All associations, except for social support, were disease transcending. This study explored factors associated with varying levels of activation for self-management. These results are a first step in supporting clinicians and researchers to identify subpopulations of chronic disease patients less likely to be engaged in self-management. Increased scientific efforts are needed to explain the greater part of the factors that contribute to the complex nature of patient activation for self-management.

BackgroundCOPD self-management is a complex behavior influenced by many factors. Despite scientific evidence that better disease outcomes can be achieved by enhancing self-management, many COPD patients do not respond to self-management interventions. To move toward more effective self-management interventions, knowledge of characteristics associated with activation for self-management is needed. The purpose of this study was to identify key patient and disease characteristics of activation for self-management.MethodsAn explorative cross-sectional study was conducted in primary and secondary care in patients with COPD. Data were collected through questionnaires and chart reviews. The main outcome was activation for self-management, measured with the 13-item Patient Activation Measure (PAM). Independent variables were sociodemographic variables, self-reported health status, depression, anxiety, illness perception, social support, disease severity, and comorbidities.ResultsA total of 290 participants (age: 67.2±10.3; forced expiratory volume in 1 second predicted: 63.6±19.2) were eligible for analysis. While poor activation for self-management (PAM-1) was observed in 23% of the participants, only 15% was activated for self-management (PAM-4). Multiple linear regression analysis revealed six explanatory determinants of activation for self-management (P<0.2): anxiety (β: −0.35; −0.6 to −0.1), illness perception (β: −0.2; −0.3 to −0.1), body mass index (BMI) (β: −0.4; −0.7 to −0.2), age (β: −0.1; −0.3 to −0.01), Global Initiative for Chronic Obstructive Lung Disease stage (2 vs 1 β: −3.2; −5.8 to −0.5; 3 vs 1 β: −3.4; −7.1 to 0.3), and comorbidities (β: 0.8; −0.2 to 1.8), explaining 17% of the variance.ConclusionThis study showed that only a minority of COPD patients is activated for self-management. Although only a limited part of the variance could be explained, anxiety, illness perception, BMI, age, disease severity, and comorbidities were identified as key determinants of activation for self-management. This knowledge enables health care professionals to identify patients at risk of inadequate self-management, which is essential to move toward targeting and tailoring of self-management interventions. Future studies are needed to understand the complex causal mechanisms toward change in self-management.

The effectiveness of heart failure (HF) self-management interventions varies within patients suggesting that one size does not fit all. It is expected that effectiveness can be optimized when interventions are tailored to individual patients. The aim of this review was to synthesize the literature on current use of tailoring in self-management interventions and patient characteristics associated with self-management capacity and success of interventions, as building blocks for tailoring. Within available trials, the degree to which interventions are explicitly tailored is marginal and often limited to content. We found that certain patient characteristics that are associated with poor self-management capacity do not influence effectiveness of a given intervention (i.e., age, gender, ethnicity, disease severity, number of comorbidities) and that other characteristics (low: income, literacy, education, baseline self-management capacity) in fact are indicators of patients with a high likelihood for success. Increased scientific efforts are needed to continue unraveling success of self-management interventions and to validate the modifying impact of currently known patient characteristics.

BackgroundSelf-management interventions are considered effective in patients with chronic disease, but trials have shown inconsistent results, and it is unknown which patients benefit most. Adequate self-management requires behaviour change in both patients and health care providers. Therefore, the Activate intervention was developed with a focus on behaviour change in both patients and nurses. The intervention aims for change in a single self-management behaviour, namely physical activity, in primary care patients at risk for cardiovascular disease. The aim of this study is to evaluate the effectiveness of the Activate intervention.Methods/designA two-arm cluster randomised controlled trial will be conducted to compare the Activate intervention with care as usual at 31 general practices in the Netherlands. Approximately 279 patients at risk for cardiovascular disease will participate. The Activate intervention is developed using the Behaviour Change Wheel and consists of 4 nurse-led consultations in a 3-month period, integrating 17 behaviour change techniques. The Behaviour Change Wheel was also applied to analyse what behaviour change is needed in nurses to deliver the intervention adequately. This resulted in 1-day training and coaching sessions (including 21 behaviour change techniques). The primary outcome is physical activity, measured as the number of minutes of moderate to vigorous physical activity using an accelerometer. Potential effect modifiers are age, body mass index, level of education, social support, depression, patient-provider relationship and baseline number of minutes of physical activity. Data will be collected at baseline and at 3 months and 6 months of follow-up. A process evaluation will be conducted to evaluate the training of nurses, treatment fidelity, and to identify barriers to and facilitators of implementation as well as to assess participants’ satisfaction.DiscussionTo increase physical activity in patients and to support nurses in delivering the intervention, behaviour change techniques are applied to change behaviours of the patients and nurses. Evaluation of the effectiveness of the intervention, exploration of which patients benefit most, and evaluation of our theory-based training for primary care nurses will enhance understanding of what works and for whom, which is essential for further implementation of self-management in clinical practice.Trial registrationClinicalTrials.gov identifier: NCT02725203. Registered on 25 March 2016.Electronic supplementary materialThe online version of this article (doi:10.1186/s13063-017-1823-9) contains supplementary material, which is available to authorized users.

Background and aimSelf-management support is an integral part of current chronic care guidelines. The success of self-management interventions varies between individual patients, suggesting a need for tailored self-management support. Understanding the role of patient factors in the current decision making of health professionals can support future tailoring of self-management interventions. The aim of this study is to identify the relative importance of patient factors in health professionals’ decision making regarding self-management support.MethodA factorial survey was presented to primary care physicians and nurses. The survey consisted of clinical vignettes (case descriptions), in which 11 patient factors were systematically varied. Each care provider received a set of 12 vignettes. For each vignette, they decided whether they would give this patient self-management support and whether they expected this support to be successful. The associations between respondent decisions and patient factors were explored using ordered logit regression.ResultsThe survey was completed by 60 general practitioners and 80 nurses. Self-management support was unlikely to be provided in a third of the vignettes. The most important patient factor in the decision to provide self-management support as well as in the expectation that self-management support would be successful was motivation, followed by patient-provider relationship and illness perception. Other factors, such as depression or anxiety, education level, self-efficacy and social support, had a small impact on decisions. Disease, disease severity, knowledge of disease, and age were relatively unimportant factors.ConclusionThis is the first study to explore the relative importance of patient factors in decision making and the expectations regarding the provision of self-management support to chronic disease patients. By far, the most important factor considered was patient’s motivation; unmotivated patients were less likely to receive self-management support. Future tailored interventions should incorporate strategies to enhance motivation in unmotivated patients. Furthermore, care providers should be better equipped to promote motivational change in their patients.